AWSML

Developers face significant challenges when using foundation models (FMs) to extract data from unstructured assets. This data extraction process requires carefully identifying models that meet the developer’s specific accuracy, cost, and feature requirements. Additionally, developers must invest considerable time optimizing price performance through fine-tuning and extensive prompt engineering. Managing multiple models, implementing safety guardrails, and … Read more

TUI Group is one of the world’s leading global tourism services, providing 21 million customers with an unmatched holiday experience in 180 regions. TUI Group covers the end-to-end tourism chain with over 400 owned hotels, 16 cruise ships, 1,200 travel agencies, and 5 airlines covering all major holiday destinations around the globe. At TUI, crafting … Read more

Today, we are excited to announce that the Llama 3.3 70B from Meta is available in Amazon SageMaker JumpStart. Llama 3.3 70B marks an exciting advancement in large language model (LLM) development, offering comparable performance to larger Llama versions with fewer computational resources. In this post, we explore how to deploy this model efficiently on … Read more

We spoke with Dr. Swami Sivasubramanian, Vice President of Data and AI, shortly after AWS re:Invent 2024 to hear his impressions—and to get insights on how the latest AWS innovations help meet the real-world needs of customers as they build and scale transformative generative AI applications. Q: What made this re:Invent different? Swami Sivasubramanian: The … Read more

Organizations are continuously seeking ways to use their proprietary knowledge and domain expertise to gain a competitive edge. With the advent of foundation models (FMs) and their remarkable natural language processing capabilities, a new opportunity has emerged to unlock the value of their data assets. As organizations strive to deliver personalized experiences to customers using … Read more

Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows … Read more

Amazon SageMaker HyperPod is designed to support large-scale machine learning (ML) operations, providing a robust environment for training foundation models (FMs) over extended periods. Multiple users — such as ML researchers, software engineers, data scientists, and cluster administrators — can work concurrently on the same cluster, each managing their own jobs and files without interfering … Read more

This post was written with Darrel Cherry, Dan Siddall, and Rany ElHousieny of Clearwater Analytics. As global trading volumes rise rapidly each year, capital markets firms are facing the need to manage large and diverse datasets to stay ahead. These datasets aren’t just expansive in volume; they’re critical in driving strategy development, enhancing execution, and … Read more

Twitch, the world’s leading live-streaming platform, has over 105 million average monthly visitors. As part of Amazon, Twitch advertising is handled by the ad sales organization at Amazon. New ad products across diverse markets involve a complex web of announcements, training, and documentation, making it difficult for sales teams to find precise information quickly. In … Read more

In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta Llama 3.1 8B model on a custom dataset. In this post, we look at the enhancements to the ModelBuilder class, which lets you seamlessly deploy … Read more

Amazon SageMaker has redesigned its Python SDK to provide a unified object-oriented interface that makes it straightforward to interact with SageMaker services. The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK (SageMaker Core) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility … Read more

We are excited to announce new features that allow creation of more powerful apps, while giving more governance control using Amazon Q Apps, a capability within Amazon Q Business that allows you to create generative AI-powered apps based on your organization’s data. These features enhance app customization options that let business users tailor solutions to … Read more

Amazon Q Business is a generative AI-powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. A large portion of that information is found in text narratives stored in various document formats such as PDFs, Word files, and HTML pages. Some information … Read more

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. In addition to planning considerations when building an AI application from the ground up, it focused on our Account Summaries use case, which allows account teams to quickly understand the … Read more

Amazon Aurora PostgreSQL-Compatible Edition is a fully managed, PostgreSQL-compatible, ACID-aligned relational database engine that combines the speed, reliability, and manageability of Amazon Aurora with the simplicity and cost-effectiveness of open source databases. Aurora PostgreSQL-Compatible is a drop-in replacement for PostgreSQL and makes it simple and cost-effective to set up, operate, and scale your new and … Read more

This post was co-written with Varun Kumar from Tealium Retrieval Augmented Generation (RAG) pipelines are popular for generating domain-specific outputs based on external data that’s fed in as part of the context. However, there are challenges with evaluating and improving such systems. Two open-source libraries, Ragas (a library for RAG evaluation) and Auto-Instruct, used Amazon … Read more

EBSCOlearning offers corporate learning and educational and career development products and services for businesses, educational institutions, and workforce development organizations. As a division of EBSCO Information Services, EBSCOlearning is committed to enhancing professional development and educational skills. In this post, we illustrate how EBSCOlearning partnered with AWS Generative AI Innovation Center (GenAIIC) to use the … Read more

Today, we are excited to announce that Pixtral 12B (pixtral-12b-2409), a state-of-the-art vision language model (VLM) from Mistral AI that excels in both text-only and multimodal tasks, is available for customers through Amazon SageMaker JumpStart. You can try this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models … Read more

In this series, we share two approaches to gain insights on multimodal data like text, images, and charts. In Part 1, we presented an “embed first, infer later” solution that uses the Amazon Titan Multimodal Embeddings foundation model (FM) to convert individual slides from a slide deck into embeddings. We stored the embeddings in a … Read more

Amazon Q Business is a generative AI-powered assistant that enhances employee productivity by solving problems, generating content, and providing insights across enterprise data sources. Beyond searching indexed third-party services, employees need access to dynamic, near real-time data such as stock prices, vacation balances, and location tracking, which is made possible through Amazon Q Business plugins. … Read more

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker, users want a seamless and secure way to experiment with and select the models that deliver the most value for their business. In the initial stages of an ML … Read more

Today, we are excited to announce that Mistral-NeMo-Base-2407 and Mistral-NeMo-Instruct-2407—twelve billion parameter large language models from Mistral AI that excel at text generation—are available for customers through Amazon SageMaker JumpStart. You can try these models with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models that can be deployed with one click for … Read more

As generative AI continues to drive innovation across industries and our daily lives, the need for responsible AI has become increasingly important. At AWS, we believe the long-term success of AI depends on the ability to inspire trust among users, customers, and society. This belief is at the heart of our long-standing commitment to building … Read more

Generative AI has empowered customers with their own information in unprecedented ways, reshaping interactions across various industries by enabling intuitive and personalized experiences. This transformation is significantly enhanced by Retrieval Augmented Generation (RAG), which is a generative AI pattern where the large language model (LLM) being used references a knowledge corpus outside of its training … Read more

Today, organizations are constantly seeking ways to use advanced large language models (LLMs) for their specific needs. These organizations are engaging in both pre-training and fine-tuning massive LLMs, with parameter counts in the billions. This process aims to enhance model efficacy for a wide array of applications across diverse sectors, including healthcare, financial services, and … Read more

This post is co-written with Abhishek Sawarkar, Eliuth Triana, Jiahong Liu and Kshitiz Gupta from NVIDIA.  At AWS re:Invent 2024, we are excited to introduce Amazon Bedrock Marketplace. This a revolutionary new capability within Amazon Bedrock that serves as a centralized hub for discovering, testing, and implementing foundation models (FMs). It provides developers and organizations … Read more

This post is cowritten with Isaac Cameron and Alex Gnibus from Tecton. Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. … Read more

Today, we’re excited to announce a new capability that allows you to deploy over 100 open-weight and proprietary models from Amazon SageMaker JumpStart and register them with Amazon Bedrock, allowing you to seamlessly access them through the powerful Amazon Bedrock APIs. You can now use Amazon Bedrock features such as Amazon Bedrock Knowledge Bases and … Read more

When using generative AI, achieving high performance with low latency models that are cost-efficient is often a challenge, because these goals can clash with each other. With the newly launched Amazon Bedrock Model Distillation feature, you can use smaller, faster, and cost-efficient models that deliver use-case specific accuracy that is comparable to the largest and … Read more

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. To address these challenges, we introduce Amazon Bedrock IDE, an integrated environment for developing and customizing generative AI applications. Formerly known as Amazon Bedrock Studio, Amazon Bedrock IDE is now incorporated into … Read more